Leveraging AI for Real World Commercial Benefits

The Curious Codex

          7 Votes   Published 2024-05-17, Updated 2024-06-16



Leveraging AI for Real World Commercial Benefits

The Author
GEN UK Blog

By Richard (Senior Partner)

Richard has been with the firm since 1992 and was one of the founding partners

Introduction

AI Bulb

Artificial Intelligence (AI) has significantly reshaped the landscape of business analytics, offering unparalleled capabilities to transform raw data into actionable insights. By automating complex processes and enabling data-driven decision-making, AI has become an invaluable asset for businesses seeking to maintain a competitive edge. This article delves into the usefulness of AI in business analytics through two specific use cases: transcribing call recordings and analysing Customer Relationship Management (CRM) data to predict customer re-purchase likelihood.

The Role of AI in Business Analytics

Business analytics involves the exploration and analysis of an organisation's data to inform decision-making and improve performance. Traditionally, this process required significant manual effort and expertise, often resulting in slow and costly operations. AI, however, revolutionises business analytics by offering:

  • Enhanced Data Processing: AI algorithms can process vast amounts of data at unprecedented speeds, identifying patterns and trends that might be invisible to human analysts.
  • Predictive Analytics: Machine learning models can predict future outcomes based on historical data, allowing businesses to anticipate trends and prepare accordingly.
  • Automated Insights: AI can automate the extraction of insights from data, reducing the need for extensive human intervention and enabling quicker decision-making.
  • Personalisation: AI-driven analytics can tailor insights and recommendations to specific business needs, enhancing the relevance and impact of the data.

These capabilities make AI a powerful tool for businesses looking to leverage data for strategic advantage.

Use Case 1: Transcribing Call Recordings

The Challenge

Customer service centres handle thousands of calls daily, generating a wealth of data that can provide valuable insights into customer needs, preferences, and pain points. However, the sheer volume of these recordings makes manual transcription impractical. Accurate transcription is crucial for analysing customer interactions, improving service quality, and training staff.

AI Solution

AI-driven speech recognition technology offers a solution by automating the transcription of call recordings. These systems use natural language processing (NLP) and machine learning algorithms to convert spoken language into text accurately and efficiently.

Implementation

  1. Data Collection: Recordings from customer service calls are collected and stored in a centralised database.
  2. Speech Recognition: AI algorithms process these recordings, converting speech into text while identifying different speakers.
  3. Text Analysis: NLP techniques analyse the transcribed text to identify key themes, sentiment, and actionable insights.
  4. Integration: The transcribed and analysed data is integrated into the company's CRM or other analytics platforms.

Benefits

  • Improved Accuracy and Efficiency: AI transcription is faster and more accurate than manual methods, reducing the time and cost associated with data processing.
  • Enhanced Customer Insights: Analysing transcribed call data can reveal common customer issues, sentiment trends, and areas for service improvement.
  • Better Training and Quality Control: Transcriptions can be used to train customer service representatives and monitor their performance, ensuring consistent service quality.

Our Client, a reputable contact centre based in the UK

By integrating AI transcription technology, our client was able to:

  • Apply natural language queries again call transcripts.
  • Identify agents who have higher rates of customer dis-satisfaction simply by analysing language used.
  • Enhance training programmes by focusing on the agents who are not following scripts correctly, or who make mistakes more often.

Use Case 2: Analysing CRM Data to Predict Re-Purchase Likelihood

The Challenge

For businesses that rely on repeat customers, understanding which customers are likely to re-purchase is crucial for effective marketing and sales strategies. Traditional methods of analysing CRM data can be labour-intensive and may not accurately predict customer behaviour.

AI Solution

AI and machine learning algorithms can analyse CRM data to predict which customers are most likely to re-purchase and which are at risk of churning. By leveraging historical purchase data, customer interactions, and other relevant metrics, these models can provide highly accurate predictions.

Implementation

  1. Data Aggregation: CRM data, including purchase history, interaction records, and demographic information, is aggregated into a centralised platform.
  2. Feature Engineering: Key features that influence re-purchase behaviour are identified and extracted from the data.
  3. Model Training: Machine learning models are trained on historical data to learn patterns and correlations that indicate re-purchase likelihood.
  4. Prediction and Segmentation: The trained models predict the re-purchase likelihood for each customer, segmenting them into different risk categories.
  5. Actionable Insights: The predictions are used to inform targeted marketing campaigns, personalised offers, and proactive customer engagement strategies.

Benefits

  • Increased Customer Retention: By identifying at-risk customers and engaging them proactively, businesses can improve retention rates.
  • Optimised Marketing Efforts: Marketing resources can be allocated more effectively by targeting customers with the highest likelihood of re-purchase.
  • Personalised Customer Experience: Tailored offers and communications based on predictive insights enhance the customer experience and drive loyalty.

Our client in the personal medical service business

Supplied their CRM data to us, and we then trained an AI model on that data including buying and spending patterns. The results included:

  • A shortlist of customers likely to re-purchase, which was then converted to a marketing campaign.
  • A shortlist of customers who were very unlikely to re-purchase, so these could be purged from the system.
  • Leading to a 60% reduction in the marketing budget by only targeting customers likely to purchase.

Once we've built the model, when this client wants us to re-analyse their CRM, its a simple job with minimal cost.

The Future of AI in Business Analytics

As AI technology continues to evolve, its applications in business analytics will become even more sophisticated and impactful. Future advancements may include:

  • Real-Time Analytics: AI systems capable of processing and analysing data in real-time, providing instant insights and enabling agile decision-making.
  • Enhanced Personalisation: More advanced algorithms that deliver highly personalised experiences and recommendations to customers.
  • Predictive Maintenance: AI-driven predictive analytics used in operational contexts, such as predicting equipment failures and optimising maintenance schedules.

Businesses that embrace these advancements will be well-positioned to harness the full potential of AI in transforming their analytics capabilities.

Conclusion

AI has already proven its worth in business analytics by automating complex tasks, providing predictive insights, and enhancing decision-making processes. The use cases of transcribing call recordings and analysing CRM data to predict re-purchase likelihood demonstrate how AI can drive significant value across different business functions. As AI technology continues to advance, its role in business analytics will only grow, offering even greater opportunities for innovation and competitive advantage.

By integrating AI into their analytics strategies, businesses can unlock new levels of efficiency, insight, and customer satisfaction, paving the way for sustained growth and success in an increasingly data-driven world.


          7 Votes   Published 2024-05-17, Updated 2024-06-16

--- This content is not legal or financial advice & Solely the opinions of the author ---


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